Malware Detection and Classification Model Using Machine Learning Random Forest Approach

Malware programs attack computer systems, smart mobile devices, and some applications. Malware is a program that needs to be watched out for because it can be a threat to computer users and internet networks. Malware was created to steal personal information about a computer user or control a user’s device over a network. Computers are easily infiltrated by various malware programs that can interfere with and even damage user files. Many users are not aware of the entry of malware programs into a computer, one of which is through a network that contains the malware program. To solve this problem, this study discusses malware detection based on network traffic and classifies these types of networks based on their groups so that they can help detect whether the network contains malware or not. The data used in this study was taken from the Kaggle Data Set, namely the Android Network Traffic Malware with a total of 7845 Data in the form of a CSV file containing a collection of data that has been captured based on traffic on the network that contains malware and does not contain malware. The process of training and testing on the Data Set is carried out using the Rapidminer Tools by making a Binary Classification or creating two classes, namely the malicious class and the benign class. The method used is Machine Learning by comparing the Random Forest Algorithm, Decission Tree and Gradient Boosted Tree. The results obtained from the three algorithms show that Random Forest has the highest level of accuracy.